class_docstr: EXPERIMENTAL
  
  AutoGluon InterpretableTabularPredictor predicts values in a column of a tabular dataset (classification or regression).
  InterpretableTabularPredictor shares the same functionality as TabularPredictor, but is limited to simple models
  that are easier to interpret visually via simple rules.
  
  InterpretableTabularPredictor should be used when accuracy is not important,
  and instead interpretability is the key requirement.
  
  Categorical features are one-hot-encoded to preserve interpretability.
  
  Stacking and bagging are not available in this predictor to preserve interpretability.
code: def _validate_fit_extra_kwargs(
      self, kwargs, extra_valid_keys=None
  ) -> dict:
      kwargs = super()._validate_fit_extra_kwargs(
          kwargs=kwargs, extra_valid_keys=extra_valid_keys
      )
      print(kwargs)
      if (
          "num_bag_folds" in kwargs
          and kwargs["num_bag_folds"] is not None
          and kwargs["num_bag_folds"] > 1
      ):
          raise ValueError(
              f"{self.__class__.__name__} does not support `num_bag_folds`."
          )
      if (
          "num_bag_sets" in kwargs
          and kwargs["num_bag_sets"] is not None
          and kwargs["num_bag_sets"] > 1
      ):
          raise ValueError(
              f"{self.__class__.__name__} does not support `num_bag_sets`."
          )
      if (
          "num_stack_levels" in kwargs
          and kwargs["num_stack_levels"] is not None
          and kwargs["num_stack_levels"] >= 1
      ):
          raise ValueError(
              f"{self.__class__.__name__} does not support `num_stack_levels`."
          )
      if "auto_stack" in kwargs and kwargs["auto_stack"]:
          raise ValueError(
              f"{self.__class__.__name__} does not support `auto_stack`."
          )
      return kwargs
  
file_path: autogluon/tabular/src/autogluon/tabular/predictor/interpretable_predictor.py
imports: import logging
  import numpy
  import pandas
  from .predictor import TabularPredictor
label_desc: Use image node_id 2 for calling the InterpretableTabularPredictor obj's underlying member method code with example usage: obj._validate_fit_extra_kwargs(kwargs, extra_valid_keys) and returns: kwargs